Instructions to use MoYoYoTech/Translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use MoYoYoTech/Translator with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf MoYoYoTech/Translator:Q5_0 # Run inference directly in the terminal: llama cli -hf MoYoYoTech/Translator:Q5_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf MoYoYoTech/Translator:Q5_0 # Run inference directly in the terminal: llama cli -hf MoYoYoTech/Translator:Q5_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf MoYoYoTech/Translator:Q5_0 # Run inference directly in the terminal: ./llama-cli -hf MoYoYoTech/Translator:Q5_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf MoYoYoTech/Translator:Q5_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf MoYoYoTech/Translator:Q5_0
Use Docker
docker model run hf.co/MoYoYoTech/Translator:Q5_0
- LM Studio
- Jan
- Ollama
How to use MoYoYoTech/Translator with Ollama:
ollama run hf.co/MoYoYoTech/Translator:Q5_0
- Unsloth Desktop
- Pi
How to use MoYoYoTech/Translator with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MoYoYoTech/Translator:Q5_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "MoYoYoTech/Translator:Q5_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use MoYoYoTech/Translator with Docker Model Runner:
docker model run hf.co/MoYoYoTech/Translator:Q5_0
- Lemonade
How to use MoYoYoTech/Translator with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MoYoYoTech/Translator:Q5_0
Run and chat with the model
lemonade run user.Translator-Q5_0
List all available models
lemonade list
- Hermes Agent
How to use MoYoYoTech/Translator with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MoYoYoTech/Translator:Q5_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default MoYoYoTech/Translator:Q5_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use MoYoYoTech/Translator with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MoYoYoTech/Translator:Q5_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "MoYoYoTech/Translator:Q5_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
daihui.zhang commited on
Commit ·
01e617c
1
Parent(s): 1bf4992
update config of save data to save flag
Browse files- config.py +3 -4
- transcribe/whisper_llm_serve.py +12 -11
config.py
CHANGED
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@@ -3,17 +3,16 @@ import re
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import logging
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DEBUG = True
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TEST = False
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logging.getLogger("pywhispercpp").setLevel(logging.WARNING)
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-
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logging.basicConfig(
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level=logging.DEBUG if DEBUG else logging.INFO,
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format="%(asctime)s - %(levelname)s - %(message)s",
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filename='translator.log',
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datefmt="%H:%M:%S"
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)
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-
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# Add terminal log
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console_handler = logging.StreamHandler()
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console_handler.setLevel(logging.DEBUG if DEBUG else logging.INFO)
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import logging
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DEBUG = True
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logging.getLogger("pywhispercpp").setLevel(logging.WARNING)
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logging.basicConfig(
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level=logging.DEBUG if DEBUG else logging.INFO,
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format="%(asctime)s - %(levelname)s - %(message)s",
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filename='translator.log',
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datefmt="%H:%M:%S"
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)
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# save pipelines data to disk
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SAVE_DATA_SAVE = False
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# Add terminal log
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console_handler = logging.StreamHandler()
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console_handler.setLevel(logging.DEBUG if DEBUG else logging.INFO)
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transcribe/whisper_llm_serve.py
CHANGED
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@@ -66,17 +66,18 @@ class WhisperTranscriptionService:
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# for test
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self._transcrible_time_cost = 0.
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self._translate_time_cost = 0.
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self.
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self.
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# self._c = 0
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def
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writer = TestDataWriter()
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while not self.
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test_data = self.
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writer.write(test_data) # Save test_data to CSV
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@@ -247,8 +248,8 @@ class WhisperTranscriptionService:
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)
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current_time = time.perf_counter()
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time_diff = current_time - start_time
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if config.
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self.
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seg_id=ana_result.seg_id,
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transcrible_time=self._transcrible_time_cost,
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translate_time=self._translate_time_cost,
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@@ -277,6 +278,6 @@ class WhisperTranscriptionService:
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"""停止所有处理线程并清理资源"""
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self._translate_thread_stop.set()
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self._frame_processing_thread_stop.set()
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if config.
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self.
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logger.info(f"Stopping transcription service for client: {self.client_uid}")
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# for test
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self._transcrible_time_cost = 0.
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self._translate_time_cost = 0.
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if config.SAVE_DATA_SAVE:
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self._save_task_stop = threading.Event()
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self._save_queue = queue.Queue()
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self._save_thread = self._start_thread(self.save_data_loop)
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# self._c = 0
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def save_data_loop(self):
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writer = TestDataWriter()
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while not self._save_task_stop.is_set():
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test_data = self._save_queue.get()
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writer.write(test_data) # Save test_data to CSV
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)
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current_time = time.perf_counter()
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time_diff = current_time - start_time
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if config.SAVE_DATA_SAVE:
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self._save_queue.put(DebugResult(
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seg_id=ana_result.seg_id,
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transcrible_time=self._transcrible_time_cost,
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translate_time=self._translate_time_cost,
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"""停止所有处理线程并清理资源"""
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self._translate_thread_stop.set()
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self._frame_processing_thread_stop.set()
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if config.SAVE_DATA_SAVE:
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self._save_task_stop.set()
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logger.info(f"Stopping transcription service for client: {self.client_uid}")
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